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cs.AI2024
PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
Jonathan Light, Sixue Xing, Yuanzhe Liu +7
Effective extraction of the world knowledge in LLMs for complex decision-making tasks remains a challenge. We propose a framework PIANIST for decomposing the world model into seven…
cs.AI2024
Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search
Jonathan Light, Min Cai, Weiqin Chen +5
Traditional reinforcement learning and planning typically requires vast amounts of data and training to develop effective policies. In contrast, large language models (LLMs) exhibi…
cs.AI2023
AvalonBench: Evaluating LLMs Playing the Game of Avalon
Jonathan Light, Min Cai, Sheng Shen +1
In this paper, we explore the potential of Large Language Models (LLMs) Agents in playing the strategic social deduction game, Resistance Avalon. Players in Avalon are challenged n…